Phishing in Wonderland: Evaluating Learning-Based Ethereum Phishing Transaction Detection and Pitfalls

Phishing attacks pose significant risks to the Ethereum ecosystem, comprising over 50% of Ethereum-related cybercrimes, leading to the emergence of many machine learningbased defenses. This paper introduces a comprehensive framework aimed at enhancing machine learning-based phishing detection in Ethereum transactions. The framework addresses critical aspects such as feature selection, class imbalance, model robustness, and algorithm optimization. By systematically evaluating the strengths and limitations of existing approaches, we highlight gaps in current practices, particularly in feature manipulation and unsustainable performance outcomes. Through both analytical and experimental assessments, we demonstrate the framework's ability to streamline detection techniques, improving generalization and model effectiveness. Our findings emphasize the importance of refining detection strategies to meet the evolving challenges posed by sophisticated phishing schemes in the blockchain space.